Why is everyone talking about GPUs these days?

One of the most talked about words in the IT industry these days is definitely GPU. From generative AI such as ChatGPT to autonomous vehicles, high-spec games, video production, metaverse, and blockchain. GPUs are always at the heart of the latest technology.

In particular, as NVIDIA's market capitalization more than tripled from 2023 to 2024, and demand for GPUs exploded, mainly in clouds and data centers, GPUs are now not just graphics cards, but an important resource that is called the “oil of the AI era.” You can also see how the computing industry, which was previously centered around CPUs, is now being reorganized around GPUs.

Why are GPUs, which were only of interest to gaming and graphics professionals in the past, now a hot topic for corporate CTOs, investors, and even the general public? In this article, I will briefly explain why GPUs are so important, what has changed, where they are being used, and in what direction they are evolving, from basic concepts to applications, options, and future trends. Let's dive into the world of GPUs together from now on.

What is a GPU?

GPU (Graphics Processing Unit) means 'graphics processing device' and was originally hardware specialized for 3D games and graphics processing. However, today's GPUs are more than just graphics-only chips, A key role in AI, data analysis, scientific computation, and any other field where parallel computation is requiredI'm doing it.

GPUs perform computation simultaneously through thousands of small cores, and are characterized by being able to quickly perform not only complex graphics processing but also numerous mathematical calculations in parallel. Thanks to these characteristics, it is widely used in various high-performance computing fields such as deep learning, autonomous driving, VFX, and real-time rendering.

CPU vs GPU: What's the difference?

We've heard a lot about CPU, but since GPU is a keyword that has recently emerged, you might be wondering what the difference is. The CPU and GPU are both processors responsible for computation, but they have essential differences in structure and processing method. A CPU is a general-purpose processor optimized for sequentially processing complex and diverse tasks with a small number of powerful cores. Excel tasks, web browsing, and operating system control that we use on a daily basis are all areas that the CPU is responsible for.

GPUs, on the other hand, are optimized for parallel processing of the same computation simultaneously through hundreds to thousands of cores. Because of this, it performs much better in tasks such as image rendering, image processing, and AI training that require simultaneous computation of large amounts of data.

The CPU is responsible for judging and directing various tasks like a 'team leader'. GPUs, on the other hand, increase efficiency by having thousands of people perform the same tasks over and over again at the same time, like 'workers on a production line'. The CPU handles tasks that require complex judgment and control, and the GPU better handles tasks that require a large number of iterative calculations. In summary, CPUs are “multitasking experts who make quick judgments from various aspects,” and GPUs are “parallel processing experts who simultaneously process the same tasks in large quantities”It can be said.

What are GPUs used for?

GPUs were originally created for games and 3D rendering, but are now being used as a core technology in various industries. Gaming sectorIn order to achieve high-resolution graphics and real-time 3D rendering, a GPU is essential.

The field of artificial intelligence and machine learningIn order to learn and infer large-scale data, the parallel computing power of the GPU is absolutely necessary. Video or image processingEven in, GPUs play an important role in processing video effects (VFX), 3D modeling, and high-resolution streaming.

Autonomous carsIn the case of, data collected from numerous sensors and cameras must be analyzed in real time, so high-speed GPU computation is essential. Blockchain miningIn, a GPU is used as a computing device to quickly process complex hash operations. The field of metaverse and virtual realityEven in, GPUs are becoming an essential element for implementing real-time interactions and immersive experiences.

Why are GPUs getting a lot of attention recently?

There are three main reasons why GPUs are attracting unprecedented attention in recent years.

First, the advent of generative AI, including ChatGPT, requires large-scale computational resourcesThis is because it has become These models learn and infer hundreds of millions of parameters, a task that would be virtually impossible without GPUs.

Second, due to the explosion of data around the world, there is a high demand for hardware that can process it quicklyThey lost, and GPUs emerged as the best alternative for this.

Third, interest in GPUs has exploded across the industry as the stock price of NVIDIA, a leading company that produces GPUs, skyrocketed along with the AI crazeI did it. In particular, cloud service providers are adopting GPU server infrastructure on a large scale, and GPUs are becoming not just hardware, but also a “GPU as a Service (GPU as a Service)” concept.

Where are GPUs going in the future?

The future of GPUs goes beyond simple graphics processing devices The direction of evolving into the 'backbone of AI infrastructure'We are moving forward to In particular, NVIDIA is focusing on developing AI-only architectures by launching GPUs specialized in AI computation, such as the H100 and B100. At the same time, Google is developing devices to replace or complement GPUs, such as TPU (Tensor Processing Unit), and Apple and ARM are developing devices to replace or complement GPUs, such as NPU (Neural Processing Unit), and competition with special purpose chips such as FPGAs and ASICs is intensifying.

Despite these changes, GPUs are still rated as the most flexible choice in terms of versatility and scalability. Technically, various innovations are underway, such as modular design using chiplet structures, high bandwidth memory (HBM3), and low power optimization. Also, the way GPUs are used is changing. The method of renting GPU instances from cloud services such as AWS, Google Cloud, and Microsoft Azure rather than purchasing equipment directly is becoming more common, and the concept of “GPU as a Service” is rapidly spreading. As such, GPUs are rapidly evolving technologically, industrially, and commercially, and are expected to continue to attract attention as a core resource for the technology industry over the next few years.

GPUs emerge as the heart of technology

We are now living in a GPU-centric era. GPUs are no longer auxiliary devices for graphics, A key driving force that accelerates AI, data, and the entire industryIt is positioned as

In particular, generative AI, like ChatGPT, which must process hundreds of millions of parameters cannot exist without GPUs. GPUs act as engines that enable current technological innovation by analyzing and processing vast amounts of data such as text, images, images, sensors, and networks at ultra-high speed. Over the next 5 years, GPUs will continue to expand beyond just hardware products to services (GPUaaS), infrastructure, platforms, and ecosystems. GPUs are being rapidly adopted not only by technology companies, but also in various industries such as manufacturing, finance, and healthcare, and this is accelerating the pace of digital transformation.

And right at this point The importance of GPU monitoring is also highlightedIt's happening. Real-time monitoring and optimization are essential to efficiently utilize high-performance GPU resources in an operating environment. Failure to monitor GPU conditions such as compute load, memory usage, temperature, and power consumption can not only waste money, but also lead to AI model performance degradation and system failure.

Especially when using cloud-based GPU instances, GPU visibility tools that can reduce costs by identifying excessive computational waste or idle resources are essential. Now that GPUs have become a core resource for corporate competitiveness, 'how to make good use' of those resources is also an important criterion for technology leadership. In the coming era, there will be more and more differences between those who know GPUs and those who are “good at handling” GPUs. Once you have a basic understanding of GPUs through this article, I recommend that you now consider how to effectively monitor and optimize GPUs.